THE APPLICATION OF NEURAL NETWORK TECHNIQUES TO PREDICTING PERSONNEL ATTRITION CAN HAVE SEVERAL ADVANTAGES OVER CONVENTIONAL APPROACHES SUCH AS TIME SERIES METHODS AND ECONOMETRIC MODELS. THE POWER OF THE NEURAL NETWORK APPROACH HAS BEEN DEMONSTRATED BY THEIR ABILITY TO REPRESENT NONLINEAR MAPPING NETWORKS, AND TO SUCCESSFULLY MANAGE MULTIVARIATE DATA. THIS PHASE I PROGRAM WILL IDENTIFY AND CHARACTERIZE THE VARIABLES THAT HAVE THE STRONGEST INFLUENCE ON AN INDIVIDUALS LIKELIHOOD OF ATTRITION. SECONDLY, A NEURAL NETWORK BASED MODEL WILL BE DEVELOPED UTILIZING THE SELECTED INPUT VARIABLES TO PREDICT PERSONNEL ATTRITION RATES. FINALLY, A THOROUGH COMPARISON BETWEEN THE DEVELOPED MODEL AND EXISTING METHODS WILL BE PRESENTED. THIS PROJECT IS EXPECTED TO PROVIDE A SOLID FOUNDATION FOR A PHASE II DEVELOPMENT OF A COMPLETE MANPOWER PLANNING TOOL. WITH THE EXPECTED INCREASE IN PERFORMANCE FROM THE PROPOSED METHOD, FOLLOW FUNDING IS FULLY EXPECTED.